Demand-Side Management Efficiency Estimation in Microgrids Performed by Smart Metering Systems

Transcription

1 International Conference on Renewable Energies and Power Quality (ICREPQ 4) Cordoba (Spain), 8 th to 0 th April, 04 exçxãtuäx XÇxÜzç tçw céãxü dâtä àç ]ÉâÜÇtÄ (RE&PQJ) ISS X, o., April 04 Demand-Side Management Efficiency Estimation in Microgrids Performed by Smart Metering Systems Vladislav O. Samoilenko, Andrew V. Pazderin Department of Automated Electrical Systems Ural Federal University named after the first President of Russia B.. Yeltsin Mira 9 str., 6000 Yekaterinburg (Russia) Phone/Fax number: , vvsamoylenko@yandex.ru, pav@daes.ustu.ru Abstract. The ongoing implementation of the energy accounting tariffs with differentiated rates, which depend upon the market conditions and may change in a short-term perspective, provide the possibility to use it as a financial incentive base of the Demand-Side Management (DSM). On the other hand, modern hi-technology energy metering and accounting systems with a large number of functions and consumer feedback are the good means of DSM. Existing systems of Smart Metering (SM) and consumer feedback are usually not fully used. So, the efforts to combine the market principle, Smart Metering and a consumer feedback are essential. The paper presents multi-purpose system of mathematical statistics and algorithms of DSM efficiency estimation useful for both the consumers and the energy companies. The estimation is performed by SM Data processing systems. The system is focused primarily on a retail market support in Microgrids due to clear and simple framework without the necessity of making large-scale calculations. It contributes to the energy efficiency and a distribution process improvement by the motivation (manual management) or by the Smart Appliances interaction (automated management). Key words Demand-side management, Smart Metering, Microgrids, Economics, Energy efficiency.. Introduction The development of market mechanisms and electricity markets of the global energy sector leads to changes in concept of microgrids control and management []. owadays many works regarding the demand-side management in microgrids are devoted to control, maintenance, operation and power quality issues [],[3]. Primarily, they are focused on particular component models consideration and corresponding electrical parameters. Also, the issue of Smart Concepts interaction is considered from this point of view [4]. evertheless, it seems to be promising to establish more generalized approach based on energy balances and combined electrical and economical parameters to be beneficial to different market participants. Advanced metering of electricity and power by accounting tariffs with multiple rates differentiated by times of the day becomes more complicated. There is the perspective for rate changes for medium and small intervals of time. Energy companies of the UK and some other countries have an experience of rate change for the medium period of time [5]. A number of government regulations support the course. On the other hand, increased development density and grids connectivity as well as increased energy consumption of large cities leads to changing the requirements for electricity metering systems. The Smart Metering concept provides for a broad implementation of modern informational and measuring components [6]. Taking this into account, the common interests of consumers and energy companies are growing. There is a need for a program of market participants information support and evaluation of electrical parameters, focused on differentiated by time intervals accounting, which is designed to incentive the participants and provide a noncontractual management of power consumption, distributed generation and load of the grid components. The penetration of these informational resources is beneficial to all the market participants [7]: ) Consumers have an opportunity to assess their power consumption and manage it automatically depending upon the rates as well as to reduce 83 RE&PQJ, Vol., o., April 04

2 their energy costs, to choose their power supplier with the most favorable terms. ) Owners of distributed generation can get the maximum profit from its use. 3) Energy companies are able to influence a demand, a proposal for the distributed generation, reducing or changing the load of the grid components. Also they have an opportunity to conduct advanced statistics of consumption as well as automation of financial and contractual processes. Existing systems of Smart Metering billing usually provide little general information about consumption curve, bills and compared data, but not the advanced statistics about the correspondence of financial and electric parameters. Also, consumer feedback is usually not fully used. So, the efforts to combine the market principle, Smart Metering and a consumer feedback are essential. Compared to the other existing approaches, the presented approach is rather simple and clear. It is based on energy balance values and aimed primarily at developing the incentives their selves and the feedback to a customer, not the models of microgrids and parameters fluctuation.. The Dual-Purpose Incentives Besides of the consumers efforts to contribute the global idea of energy efficiency and ecology improvement, the main incentive for the consumers is an opportunity to decrease their energy costs. An analysis of the potentials to decrease the costs is aimed at the determination of rates schedule and a consumer s load curve correspondence extent. Also the analysis includes the influence of technical losses. A case when there are M different rates (or prices of a complex rate) offered to consumer is considered. All the rates (or prices) can be ranged using their values by the principle cheap expensive. The most expensive rate j may be assigned to the effectiveness of α max 0%, the cheapest - α min 00%. Rates with an intermediate price p j can be assigned to an intermediate value of α j, proportional to the relative price difference between this value and the minimum price to the price difference of these two extreme rates. The principle of ranking is shown in Figure. There are three rates: night (from 3:00 to 07:00), semipeak (from 0:00 to 7:00 and from :00 to 3:00) and peak (from 07:00 to 0:00 and from 7:00 to :00). The basic parameter of rate j efficiency a can be defined as normalized relative value obtained by minimum c min and maximal c max prices of night and peak rates, correspondingly: a ( с ( с с ) ( с 00% ) ( с с ), ) j min max j j j max сmin max сmin... M Weight-average rate use efficiency index e is one of the most important parameters. It is based measured energy values W j (or relative energy values W rj ) obtained by SM: M M M e a j Wrj a j W j W j j j j, () Thus, the coefficient of efficiency e is the weighted average of the rates. Its value represents the opportunity of savings for the consumer. There are two extreme cases: ) All the electricity was consumed during rate period with α max 0%, which means the consumer didn t use the opportunity of savings. ) All the electricity was consumed during rate period with α min 00%, which means the user used all rate for savings. The actual value of the coefficient of efficiency rates is between 0% and 00%. The weight-average rate use efficiency index e has a triple meaning: ) Utilized economy fact percentage. ) Rates schedule and a consumer s load curve correspondence extent. 3) The elasticity of a demand. The consumer is able to estimate his savings and choose the most appropriate to him rate. The estimations are suitable for a-priori and a-posteriori dynamic rate selection. The energy company is able to estimate demand-side management efficiency due to the fact that rate use efficiency index is approximately equal to the elasticity of a demand. () Using the rate use efficiency index e, it is possible to determine the economy E and lost savings E l : Fig.. The rate efficiency. E l e E W ( с max с min ) (3) 00 e W ( с max сmin ) (4) RE&PQJ, Vol., o., April 04

3 Another important parameter is the smoothing of load curve. It contributes to equipment lifetime and durability increase as well as peak lines load decrease, so it is very important. From the point of view of Math Statistics the load change can be characterized by standard variation coefficient v. But this parameter is not demonstrative even for engineers. One of the ideas how to estimate the variation using this parameter like an incentive for a consumer is to show him the economy based on the losses decrease. The losses in cables and wires connecting a consumer to electricity mains are considered. From the theory of electrical power engineering it is well known that the minimum of losses during a period of time corresponds to the case when the load flows are equal at all the parts of the period. It is useful to estimate relative reduction of the loss for single rate and the relative reduction of losses during of time period of n intervals, correspondingly: r t + v с W r с W av v 00% + v Wavj n j j Wavj n j j v j ( + v ) j 00%, 00%. where v - coefficient of consumption variation; W av losses for the case of absolutely uniform consumption; W av - losses for the case of an arbitrary consumption. Studies and the calculations show that during the day typical v vary from 0,3 to 0,75 for different rate periods and losses decrease is from 7 % to 30 %, correspondingly. Taking into account that typical losses level is about 0 % for buildings wiring, savings will be from 0,7 % to 3 %. Sometimes the problem at hand is not the value of maximum power consumption itself, but the dynamics of load increase at peak times. The concept of easy load curve smoothing by means of Smart Appliances control centre and Feedback centre interaction is considered (Figure ). (5) (6) The system should automatically account the warm-up durability which is required for the equipment, and to compare it with the time rate change time. In everyday life for consumers this turns out to be convenient - the opportunity to receive the result of the electrical installations at the time of morning rise or production process run. In the first case (Figure ) readiness time is equal to the time rate of change. From the point of view of energy company as well as the consumer it is efficient to switch on the device so that the power consumption accounted by cheaper rate (area ). Operation is planned in such a way that the time of a rate of change is also a readiness time of a process. When the readiness time comes later than time of rate change (the second case), it is profitable that electrical energy is accounted partially by cheaper rate (area ) and partially by more expensive rate (area 3). The condition of profitable operation: С 4 С3 С (7) where C 4 are the costs of equipment downtime (e.g., losses), C 3 -C is a profit gained by the rate difference. 3. Private Distributed Generation Estimation The particular installations of dispersed generation are under consideration. Such installations are often privately owned and have small power. First of all, the two effects of the introduction of consumers generation cause increased attention of energy companies: ) The effect of masking the load when the sudden shutdown of consumers own generation may results in feeders overload due to the fact that they was not designed for full capacity. ) The effect of the consumers generation far exceeding the demand (e.g. during the night) can cause increased losses and undesirable reverse flows of a great value. The RMS-imbalance of generation to the load shows the extent of balancing during M periods of time: W i W b i Wgi gi 00%, (8) where W i is load during a single period of time, W gi is generation during a single period of time. For the case when the imbalance value is far from zero, a permanent connection to the main grid being a damping component is necessary for the potential island of load and generation. Fig.. The planning principle. One-time maximum deviation of the generation from load: 85 RE&PQJ, Vol., o., April 04

4 Wgi Wi D max 00%, Wi (9) This parameter shows the extent of instantaneous damping necessity during some particular periods of time and characterize feeders maximum load. estimation useful for the consumers, distributed generation and the energy companies. The estimation is performed by SM Data processing systems. The structure of interaction is shown in Figure 3. It is known that from the point of view of distribution utilities the benefits from the presence of dispersed generation is primarily consist of load coverage and feeders flows reduction. It results at the possibility of postponing capital investments in grid reinforcement. Maximum feeders load corresponds to the period of load curve maximum, especially at the post-failure conditions or at the repair schemes. Demand for dispersed generation and own generation of consumers in these conditions increases. That s why the dual-purpose incentives include operation time during the period of the peak rate, the share of electrical energy produced during the period of the peak rate and the income from the generation of electricity. T T max st tmax Tmax 00%, (0) where T max is a duration of the peak rate (special or postfailure rate), T st is a downtime of dispersed generation installation during the period of the peak rate. The share of electrical energy produced during the period of the peak rate: d max nmax i W gi ( P n ) 00%, () g where n max is a number of single time periods during the peak rate, P g is a generation power. Using the high-price rate c max it is possible to calculate the revenue R as a product of c max and W gi as well as lost revenue due to equipment downtime: R max nmax dmax l cmax W gi 00 i, () The mentioned functions and components of statistics are also available using the Individual Statement. 4. Estimations Performed by Smart Metering The DSM estimation includes the multi-purpose system of mathematical statistics and algorithms of DSM efficiency Fig. 3. The structure of interaction. The cores of automated remote metering and advanced metering infrastructure are not shown in the Figure 3. The system is focused primarily on a retail market support. It contributes to the energy efficiency and a distribution process improvement by the motivation (manual management) or by the Smart Appliances interaction (automated management). The feedback of SM can be integrated with Smart House and Smart Energy. In a manual mode feedback data are outputted to Individual Statement in easy-understandable form, in automatic mode data are used by Smart Appliance control center through WLA/HA. The system includes the unit of dynamic rate change depending upon outer parameters. 5. Case Study The system proposed is planned to be applied for the accounting system of Ural Federal University study building o. 8 and its campus and to contribute to The University energy efficiency improvement. So, the calculation for the perspective efficiency was made. The load curve of a domestic building belonging to the University campus and study building o. 8 is shown in Figure 4, a and 4, b, correspondingly RE&PQJ, Vol., o., April 04

5 W, % W, % c, c, Rate Three periods Time period a) b) Fig. 4. The load curves Table I. Domestic building DSM efficiency estimation Month consumpt., kw*h Rate value, /kw*h RE α, % Peak 36, 6,80 0,0 Semi-peak 30,6 4,65 53,3 ight 33,,77 00,0 RUC ε, % 35,7 Cost (α49,9), 608, (435,5) Economy lost, 776,49 (603,9) One period Day 300,0 5,94,3,3 78,0 950,4 0 Table II. Study building o. 8 DSM efficiency estimation Relative economy, % 9,9 (9,4) Rate Three periods Two periods Time period Month consumpt., kw*h Rate value, /kw*h RE α, % Peak ,3 0,0 Semi-peak 8 6,40 8,0 ight 668 4,9 00,0 Day ,68 60,3 ight 668 4,9 00,0 RUC ε, % Cost, Economy lost, Relative economy, % 37, , , The data for these two buildings are presented in Table I and Table II, correspondingly. The extent of load curve (black colour) to rate curve (grey colour) correspondence can be well seen in Figure 4. The one period price for household building is shown as dashed grey line as well as day price for study building o. 8, if a case of two periods rate is considered. As it can be seen, the DSM efficiency system estimates that the real efficiency for household building was about % due to one period rate, but dynamic changing the rate and making the consumption efficiency close to 49,9 % by means of Smart Appliances use gives sufficient economy of 9,4 % (9,9 by means of dynamic rate selection only). Vice versa, a simple rate was chosen for the study building o. 8 due to it s specific load curve. Taking into account the relation between the prices of the three period rate and two period rate, it is more profitable to select the last one. It gives economy of 4,%. 6. Conclusion A new generalized approach to the DSM efficiency estimation is proposed. Compared to the other existing approaches, it is clear and rather simple. It is based on energy balance values and aimed primarily at developing the incentives their selves and the feedback to a customer, not the models of microgrids and parameters fluctuation. The entire system of the mathematical statistics including the dual-purpose incentives was established (Table III). Parameter Table III. Important Parameters Sign Rate efficiency a [%] Rates utilization coefficient e [%] Utilization Energy Compani es Price signal Demand flexibility Demand Side Incentives Economy potential 87 RE&PQJ, Vol., o., April 04

6 Parameter Sign Energy Compani es Utilization Demand Side Economy E [ ] - Utilized Loss of economy E l [ ] - Missed Average consumption W av Balances, [kw*h] statistics General Consumption variation coefficient ν [r.u.] Statistics - Relative losses reduction Balances, Economy r [%] due to load curve leveling statistics potential Percentage of the load Balances, covered by distributed cv [%] statistics generation Utilized Generation-to-load average Line av. b [%] unbalance load Missed One-time relative Line max. generation-to-load D [%] load unbalance - Absence of idles during the Offer t high-price rate max [%] flexibility General Percentage of generated electrical energy value in d max Offer comparison with the max. [%] flexibility General value Profit gained by distributed gener. during the highprice R [ ] - Utilized rate Loss of profit R l [ ] - Missed References []. Hatziargyriou, A. Dimeas and A. Tsikalakis: Management of microgrids in market environment, in Proc. of IC on Future Power Systems 005, Vol.0, pp. -7 [] S. Djokic and I. Papic: Smart Grid Implementation of Demand Side Management and Micro-generation, International Journal of Energy Optimization and Engineering, Vol.0, o.00, pp. -9 (0) [3] A. Gomes, A. Soares and C. Antunes: Impacts of demand side management and micro-generation units on low voltage distribution radial networks, in Proc. of th IC on Electrical Power Quality and Utilisation 0, Vol.0, pp. -7 [4] A. Molderink, V. Bakker and M. Bosman: Management and Control of Domestic Smart Grid Technology, IEEE Transactions on Smart Grid, Vol.0, pp (00) [5] D. Infield, J. Short and C. Home: Potential for Domestic Dynamic Demand-Side Management in the UK, in Proc. of IEEE Power Engineering Society General Meeting 007, Vol.0, pp. -6 [6] L. Osika: Calculation methods of Smart Metering for the goals of energy accounting and energy efficiency, MEI, 4 pages (03) [7] V. Samoylenko and A. Pazderin : Retail market participants support by means of Smart Metering systems, in Proc. of Electrical Power Engineering IC 0, Vol.03, o.00, pp The system of DSM efficiency estimation provides a number of benefits to different parties of electricity market. ) Consumers are motivated by financial incentives and informed by advanced analysis information about rates, economy, and schedule. ) Energy companies are informed about the consumers response, load parameters and they have an opportunity to smooth load curve by means of appropriate rate signals. 3) The social aspect includes sufficient energy efficiency improvement. 4) Distributed generation has an opportunity to gain the maximum profit. 5) Clear and simple framework is useful for Microgrids with the huge number of elements complicating large-scale calculations. All the information contained in the evaluation is both engineering and motivational (incentive) character. On the one hand, formal recommendations to the consumer are necessary as clarification of opportunities to improve the quality of consumption; on the other hand, a simple formal approach is not comprehensive. The issuance of such recommendations should be implemented with care and some of the issues of engineering psychology should be used. Strictly speaking, the recommendations should be linked to the technical, economic, operational and marketing issues facing the energy company at the moment of the recommendations issuance RE&PQJ, Vol., o., April 04